{"id":"https://openalex.org/W2593564159","doi":"https://doi.org/10.1109/isscc.2017.7870353","title":"14.5 Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI","display_name":"14.5 Envision: A 0.26-to-10TOPS/W subword-parallel dynamic-voltage-accuracy-frequency-scalable Convolutional Neural Network processor in 28nm FDSOI","publication_year":2017,"publication_date":"2017-02-01","ids":{"openalex":"https://openalex.org/W2593564159","doi":"https://doi.org/10.1109/isscc.2017.7870353","mag":"2593564159"},"language":"en","primary_location":{"id":"doi:10.1109/isscc.2017.7870353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isscc.2017.7870353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Solid-State Circuits Conference (ISSCC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://lirias.kuleuven.be/handle/123456789/579133","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061910461","display_name":"Bert Moons","orcid":"https://orcid.org/0000-0002-0136-8232"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Bert Moons","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074384348","display_name":"Roel Uytterhoeven","orcid":"https://orcid.org/0000-0001-8705-6784"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Roel Uytterhoeven","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076274517","display_name":"Wim Dehaene","orcid":"https://orcid.org/0000-0002-6792-7965"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Wim Dehaene","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012150553","display_name":"Marian Verhelst","orcid":"https://orcid.org/0000-0003-3495-9263"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Marian Verhelst","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061910461"],"corresponding_institution_ids":["https://openalex.org/I99464096"],"apc_list":null,"apc_paid":null,"fwci":22.2413,"has_fulltext":false,"cited_by_count":470,"citation_normalized_percentile":{"value":0.99579912,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"246","last_page":"247"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.786501407623291},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7477450966835022},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7244238257408142},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6895953416824341},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.5959608554840088},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.547692060470581},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5206050872802734},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.4957166910171509},{"id":"https://openalex.org/keywords/application-specific-integrated-circuit","display_name":"Application-specific integrated circuit","score":0.4716678857803345},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.44888535141944885},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4194956123828888},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4111877679824829},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.401888370513916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39339661598205566},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.373250275850296},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3265616297721863},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.3102172613143921},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.27307868003845215},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11129572987556458},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07756733894348145}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.786501407623291},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7477450966835022},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7244238257408142},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6895953416824341},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.5959608554840088},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.547692060470581},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5206050872802734},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.4957166910171509},{"id":"https://openalex.org/C77390884","wikidata":"https://www.wikidata.org/wiki/Q217302","display_name":"Application-specific integrated circuit","level":2,"score":0.4716678857803345},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.44888535141944885},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4194956123828888},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4111877679824829},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.401888370513916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39339661598205566},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.373250275850296},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3265616297721863},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3102172613143921},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.27307868003845215},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11129572987556458},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07756733894348145},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isscc.2017.7870353","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isscc.2017.7870353","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Solid-State Circuits Conference (ISSCC)","raw_type":"proceedings-article"},{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/579133","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/579133","pdf_url":null,"source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, 6-8 February 2017","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:lirias2repo.kuleuven.be:123456789/579133","is_oa":true,"landing_page_url":"https://lirias.kuleuven.be/handle/123456789/579133","pdf_url":null,"source":{"id":"https://openalex.org/S4306401954","display_name":"Lirias (KU Leuven)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I99464096","host_organization_name":"KU Leuven","host_organization_lineage":["https://openalex.org/I99464096"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, 6-8 February 2017","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8999999761581421,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320307102","display_name":"Intel Corporation","ror":"https://ror.org/01ek73717"},{"id":"https://openalex.org/F4320321730","display_name":"Fonds Wetenschappelijk Onderzoek","ror":"https://ror.org/03qtxy027"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2070167224","https://openalex.org/W2919115771","https://openalex.org/W2963893493","https://openalex.org/W3024621361","https://openalex.org/W6717256623"],"related_works":["https://openalex.org/W3090300519","https://openalex.org/W4250401876","https://openalex.org/W2943851981","https://openalex.org/W2095299560","https://openalex.org/W2907667791","https://openalex.org/W3047461507","https://openalex.org/W3126390843","https://openalex.org/W4245880644","https://openalex.org/W4312415459","https://openalex.org/W3083225868"],"abstract_inverted_index":{"ConvNets,":[0],"or":[1],"Convolutional":[2],"Neural":[3],"Networks":[4],"(CNN),":[5],"are":[6,37],"state-of-the-art":[7,56],"classification":[8],"algorithms,":[9],"achieving":[10,33,102],"near-human":[11],"performance":[12],"in":[13,27,40,119],"visual":[14,25,117],"recognition":[15,35,91,109,118],"[1].":[16],"New":[17],"trends":[18],"such":[19],"as":[20,44],"augmented":[21],"reality":[22],"demand":[23],"always-on":[24,82,116],"processing":[26],"wearable":[28,120],"devices.":[29,121],"Yet,":[30],"advanced":[31],"ConvNets":[32],"high":[34],"rates":[36],"too":[38],"expensive":[39],"terms":[41],"of":[42,52,67,77,89],"energy":[43],"they":[45],"require":[46],"substantial":[47],"data":[48],"movement":[49],"and":[50,59,111],"billions":[51],"convolution":[53],"computations.":[54],"Today,":[55],"mobile":[57],"GPU's":[58],"ConvNet":[60,100],"accelerator":[61],"ASICs":[62],"[2][3]":[63],"only":[64],"demonstrate":[65],"energy-efficiencies":[66],"10's":[68],"to":[69,105],"several":[70],"100's":[71],"GOPS/W,":[72],"which":[73],"is":[74],"one":[75],"order":[76],"magnitude":[78],"below":[79],"requirements":[80],"for":[81],"applications.":[83],"This":[84],"paper":[85],"introduces":[86],"the":[87,95],"concept":[88],"hierarchical":[90],"processing,":[92],"combined":[93],"with":[94],"Envision":[96,113],"platform:":[97],"an":[98],"energy-scalable":[99],"processor":[101],"efficiencies":[103],"up":[104],"10TOPS/W,":[106],"while":[107],"maintaining":[108],"rate":[110],"throughput.":[112],"hereby":[114],"enables":[115]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":43},{"year":2022,"cited_by_count":53},{"year":2021,"cited_by_count":65},{"year":2020,"cited_by_count":81},{"year":2019,"cited_by_count":72},{"year":2018,"cited_by_count":74},{"year":2017,"cited_by_count":13},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
